Bayesian resources

OpenBUGS www.openbugs.net

• Successor of (now discontinued) WinBUGS.
• Made Bayesian analysis popular.
• Has a GUI or can be commanded from R.
• Great example models.
• Useful for spatial analysis.
• Difficult to debug, somewhat unpredictable.

R packages for OpenBUGS

• BRugs - gives full control over OpenBUGS
• R2OpenBUGS - simpler and more user-friendly

JAGS mcmc-jags.sourceforge.net/

• Just Another Gibbs Sampler.
• Reliable, cross-platform.
• Uses the BUGS language (almost identical to OpenBUGS).
• Can be commanded from within R.

R packages for JAGS

• rjags - gives full control over JAGS
• R2jags - simpler and more user-friendly
• runjags - simple, parallel-friendly

STAN - mc-stan.org

• The newest and fastest one.
• Can be commanded from R, Python or Linux shell.
• Does not use the BUGS language, but the code is similar.
• The future of MCMC.

R packages for STAN

• RStan

INLA - www.r-inla.org

• Integrated Nested Laplace Approximation
• Very fast
• Whole different universe – not treated in this course.
• Not as flexible as MCMC (yet?).

R packages for INLA

• RINLA

Other useful R packages - see the CTAN taskview

• coda – handles and summarizes MCMC chains
• HDInterval – High Density Intervals
• MCMCglmm – simple Bayesian GLM
• spBayes and geoRglm – spatially explicit modelling
• hSDM – Hierarchical Species Distribution Modelling
• ggmcmc – pretty output visualization in ggplot2 style

• The CTAN taskview - essential R packages.
• Andrew Gelman's blog.
• J.M. White's tutorial on setting up R & JAGS.
• N.T. Hobbs' An ecological modeler's primer on JAGS
• Ben Bolker's Ecological Models and Data in R - free manuscript.
• User manuals for OpenBUGS, JAGS and STAN
• R-bloggers and Cross Validated.